General purpose graphic processing unit implementation of adaptive pulse compression algorithms

Jingxiao Cai; Yan R. Zhang

doi:10.1117/1.JRS.11.035009

17 August 2017 General purpose graphic processing unit implementation of adaptive pulse compression algorithms

Jingxiao Cai, Yan R. Zhang

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Journal of Applied Remote Sensing, Vol. 11, Issue 3, 035009 (August 2017). https://doi.org/10.1117/1.JRS.11.035009

Abstract

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Jingxiao Cai and Yan R. Zhang "General purpose graphic processing unit implementation of adaptive pulse compression algorithms," Journal of Applied Remote Sensing 11(3), 035009 (17 August 2017). https://doi.org/10.1117/1.JRS.11.035009

Received: 11 April 2017; Accepted: 18 July 2017; Published: 17 August 2017

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